{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib初相识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 认识matplotlib"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Matplotlib是一个Python 2D绘图库\n",
    "Matplotlib可用于Python脚本，Python和IPython Shell、Jupyter notebook，Web应用程序服务器和各种图形用户界面工具包\n",
    "我们所熟知的pandas和seaborn的绘图接口其实也是基于matplotlib所作的高级封装"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个最简单的绘图例子"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Matplotlib的图像是画在figure上，每一个figure又包含了一个或多个axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T06:03:02.738127Z",
     "start_time": "2020-12-14T06:03:02.274880Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T06:03:02.959932Z",
     "start_time": "2020-12-14T06:03:02.787402Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2bdca797d60>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dSthZWZjFwJCfypMabCUXpwAXR3T2DfLCkUauXzKLuDi1T863slCDrWRkCnBxxLMHGxgY8sf87JOLmT4tGXd2qk5kyiUpwMURT1XWM2NaMisLNAftYjzuLA22kktSgMuk6xkYYtuhBrVPRuApdNHSPUB1U7fTpUiYUoDLpHvuUCN9g37NPhnBmcFWu9QHl4tQgMukK6vwkj01iSvmZjldSlgrykkjMzVRN3iQi1KAy6TqG/Tx7MEGrlsyi3i1Ty4pLs7gKXTpShS5KAW4TKrnDzfSM+DT7JNRWlmYRXVTN81d/U6XImFIAS6TakuFF1dqIlcWqX0yGmf74LqcUC5AAS6Tpn/Ix9MHGthUOovEeH3rjcZlczJIio9TgMsF6adIJs2LR5ro6h+K6TvPj1VKYjyX5WXoFmtyQQpwmTRlFfWkpySwtjjH6VIiiqfQReXJDvoGNdhK3kgBLpNiYMjP1v31XFs6i6QEfduNxcpCFwM+PxUabCXn0U+STIqXqpro6BviRrVPxkyDreRiFOAyKbZU1JOWnMBbStQ+GavstGSKpk+lXH1wOY8CXEJu0Ofnz/vr2bBoBskJ8U6XE5E8hS521bbi92uwlbxOAS4ht6O6hbaeQc0+mQBPYRZtPYNUN3U5XYqEEQW4hFxZpZfUpHjWL5judCkRa6VbfXB5MwW4hJTPb/nLvnreunAGKYlqn4xXUc5UsqYmsVMBLudQgEtI7axpoalrQLNPJsgYw8pCl+5UL2+gAJeQ2lLhJSUxTu2TIPAUuqhp7qGxU4OtZJgCXELG77dsqaxn/fwZTE1OcLqciKfBVnI+BbiEzO7aVho6+zX7JEiWzMkgKSFO14PLWQpwCZmyinqSEuK4ZuEMp0uJCskJ8SzNy9Cd6uUsBbiExHD7xMvVJdOZlpLodDlRY2VhFvtOtWuwlQAKcAmRvXVteNv7NPskyFa5XQz6LHtPtDldioQBBbiExJbKehLjDRsWzXS6lKhydrCV2iiCAlxCwFpLWYWXt8zLIWOK2ifBlJmaxLwZaTqRKYACXEKg8mQHda293HCZFu+EgqfQxa7jGmwlowhwY8wjxpgGY0zlOduyjDFbjTFHAh9doS1TIklZpZeEOMOmUrVPQsHjzqKjb4gjDRpsFetGcwT+GHD9edu+CDxjrS0Bngn8XQRrLVsqvKwpziYzNcnpcqKS52wfXG2UWDdigFtrXwDO/065GXg88PnjwDuDW5ZEqgPeTmqae7hR7ZOQKcxOJSctiV0abBURfH7La3Vt9AwMBf21x7u+eaa11gtgrfUaY7RSQwDYUuklzqD2SQidGWylK1HCk99vOVjfyUtVTbxc3cyOYy109g3x6AdX8dYgL2oL+YAKY8xdwF0ABQUFod6dOKyswsuVc7PJTkt2upSotsqdxZ/3naaho48Z6SlOlxPTrLUcaehie1Uz26ua2XGsmdaeQQDc2am87fLZrC7KZll+ZtD3Pd4AP22MmR04+p4NNFzsgdbazcBmAI/Ho9PmUezI6U6qGrv54Fq306VEvXOvB1e7anJZa6lu6h4O7OpmdlQ309Q1AMCczClsWDSTNUXZrCnOJjdzSkhrGW+A/wG4E/hG4OPvg1aRRKyyinqMgesWa/VlqC3OzSA5IY7yGgV4qFlrqW3pORvY26uaaQiM9J2VnsJVJdPPBnZ+Vuqk1jZigBtjfgGsB3KMMXXA1xgO7l8ZYz4C1ALvCWWREhm2VHpZVZilX+knQVJCHEvzM3WDhxCpa309sF+uauZUex8AOWnJrCnOPhvY7uxUjDGO1TligFtrb7vIlzYEuRaJYFWNXRys7+Rrby91upSYscrt4kfPV9MzMERqkuatT8Tpjj62VzXzUlUT26ubOdHSC0DW1CRWF2XxiUBgF09PczSwz6f/6xIUT1XWA3D9ErVPJounMAufv4o9J9pYW5zjdDkRpbGzn5erXz/Crm7qBiBjSiJXzs3iw+vmsqY4m/kzphEXFz6BfT4FuARFWYWXFQWZzM4I7Ukbed2KgsAdempaFeAjaOkeYEf16z3sM6tY05ITuGJuFrddUcCa4mwWzU4nPowD+3wKcJmw483d7DvVwVdvWuR0KTElIzWR+TPTdD34BbT3DLLj2OuBfbC+E4DUpHg87izevSKPNcXZLMlNJyE+ckdCKcBlwraofeIYjzuL/95zCp/fRtSRY7B19g2ys6bl7InHfac6sBaSE+LwuF3ct2k+a4qzuTwvk8QIDuzzKcBlwrZUeFmal0Gea3IvoZLhuSg/31HL4dOdLJqd7nQ5k6ZnYIjymla2VzfzUlUzlSfb8fktSfFxLC/I5O4NJawpymZZQSbJCfFOlxsyCnCZkLrWHvbWtfPFGxY6XUpM8hRmAcMLeqI5wPsGfew+3nq2JbK3ro1BnyUhzrAsP5NPri9mTVE2KwpdpCRGb2CfTwEuE3Lm6pMb1D5xRH7WFKZPS2ZXTQt3rC50upyg6R/ysae27Wxgv1rbxoDPT3ycYcmcDD56VRFrirLxuF0xfQll7P6XS1CUVXhZnJtOYfZUp0uJScYYVrld7IzwyYSDPj+v1bWd7WHvOt5K36AfY2Bxbjp3ri1kTXE2q9xZukn2ORTgMm7e9l5217bxhesWOF1KTFtZmEVZRT317X3MyoiMVbBDPj+VpzrOBnZ5TQs9Az4AFs6aNnxZX1E2V87NJiNVgX0xCnAZN7VPwsO5N3h42+W5DldzYT6/5YD39cDeeayFzv7h+dglM9L4+5V5w4FdlE3WVN0IZLQU4DJuWyrqWThrGkXT05wuJaaV5qYzJTGe8prWsAlwv99yuKHznBGrLbT3Do9YLcqZytuX5bKmKJvVRdlMn6bRw+OlAB8lay2ffGI3x5t7+OyGeWwqnRXWS2xDqa1ngIf+Ws3O4y3cvaHE6XJiXmJ8HMvyMx29xZq1lqrGrtcHQFW30NI9PGI1P2sK1y2eydriHFYXZUdMmycSKMBH6dmDDWyprMeVmsj//NluSmenc++189m4aEZYDbcJpfbeQR5+8RiPvniMroEhbrpsNh9aN9fpsgTwuF388LkquvuHmJoc+h9ray01zedM7KtupjEwYjU3I4X1C14fsar1AaGjAB+FQZ+fr5cdoChnKmV3X0VZhZfvPnOEj/2knMvzMrj32vmsnz89aoO8s2+Qx/5Ww0N/raajb4gblszino3zWTBrmtOlScDKQhc+v2XPiTbWzQvNXJQT583Eru8YHrE6Y1oya88ZsVqQ5eyI1ViiAB+Fn++opbqxm4c+4CElMZ53r8jj7Utz+d3uk3zv2SN86NGdLC/I5HPXzuct83Ki5pu3u3+Ix7fXsPmFatp6Brm2dCb3bCxhcW6G06XJeVYUujAGdta0BC3Ave29gRGrw4F9sm14xGr21CRWnxPYRTlTo+Z7PtIowEfQ3jvId54+zJqibDYuev2GpInxcdy6Kp93Lp/Db3bV8f1nj3DHw6+wyu3i3mvnR/R0uN4BHz99uYYfPV9NS/cA1yycwb0b53NZnoI7XKWnJLJg5jR2TWCwVUPn8EzslwNH2DXNPQBkpg6PWP3YVXNZOy+HkhnhNRM7linAR/CDbUdp6x3kKzctuuA3bVJCHLdfWcAtK+fwq50n+P62o9z+0A7WFGVz77XzuWJulgNVj0/foI8ndtTy4HNVNHX1c/X86dy7sYTlgbGlEt48bhf/9eroB1s1d/XzcnUL26ub2F7VTFXj8EzsaSkJXDk3i/evHl48s2hWesyesA93CvBLON7czWN/q+HvV+SxZM6ljz6TE+K5Y42b93jy+cUrtfxgWxW3/sd2rirJ4Z6N88/ehDYc9Q/5+OUrJ/jBtqM0dPaztjibB9+/glXuyPnHR4bnovzs5VoO1ndcsM3V1jPAy9UtZ4+wD50eHrE6NSmeVXOzuNWTz5ribBbnZsT0ZMNIogC/hG8+dZD4OMN9Y1hpmJIYz4fWzeW9qwr42cvH+dHzVdzy4EusXzCdezfOZ2l+ZugKHqOBIT+/3nWC7z97FG97H1e4s/jue5ezpjjb6dJkHM7eqb6mlcW5GXT0DbLzWMvZHvaB+uERqymJcXgKs3jHslzWFGdz2ZyMqBqxGkuMtXbSdubxeGx5efmk7W8idta08J4fbefejfO5e+P4r3Xu7h/iJ9uP8x8vVNHWM8jGRTO4Z+P8EY/oQ2nQ5+fJ3XV875mjnGzrZUVBJp/ftIC1xdnqbUYway1r/u1ZpibHk5acQMXJdvx2uM23oiCTNUU5rCnOZml+RlSPWI1Gxphd1lrPm7YrwN/M77e864d/43RHP8/e93dBmXbW2TfI4y8NX9HR0TfEdYtncs/G+ZM6AnTI5+e/9pzie88cobalh6V5GXxu0wKuLomeK2di3ZeefI3f7KpjWX7m8ErH4mxWFMTWiNVopAAfg/969ST3/Ocevv2epdyyMi+or93RN8gjLx7j4b8eo7N/eDHMPRtLKJkZumuqfX7LH187xXefPkJ1UzeLc9P53LXzuWZh7CxCihV+v2XQ79cRdpRRgI9S74CPa779HNlpSfzhU28J2dn3tp4BfvzXYzz6t2P0DPp4x9JcPruhhOIgzhXx+y1llV6+8/QRjjZ0sXDWNO69dj6bSmcquEUiyMUCXCcxz/Pwi9V42/t44H8sC+mlU5mpSdx33QI+/Ja5bH6hmsdfquG/957incvn8NlrSnDnjH++tt9v+cv+eh7YeoRDpzspmZHGD9+3gusXx+78FpFopCPwczR09vHW//sc6+blsPkDb/rHLqSauvr5j+er+Mn24wz5LbesmMNnrikhP2v0cySstTx9oIEHth5mv7eDopyp3L2xhLddnqvLwkQimI7AR+GBrYfpH/LzpRsXTfq+c9KS+cpNpXzsqiIefL6KJ3bU8uTuk9y6Kp9PvXUeczKnXPS51lqeO9TIA08f5rW6dgqzU7n/1qW8Y2kuCbo8TCRqKcADDng7+M+dJ/jg2rnMnUD7YqJmpKfwtbcv5uNXF/ODbUf55c5aflNex3uvyOeT6+e9YRSntZa/Hmni/q2H2XOijTzXFL7195fz7uVzFNwiMUAtFIaD8I6HX6HiZDvPf2E9manhc0eQk229fP/Zo/y6/ARxcYb3XVnAJ9YXc7Shiwe2HmZnTSu5GSl8ZkMJt6zIIylBwS0SbdRCuYTnDjXy4tEm/vFtpWEV3gBzMqfwb+++jE+uL+b/PXuEn2w/zk8DffKZ6cn8y82LuXVVvi4bE4lBMX8EPujzc/13XsBv4c/3XB32R7A1Td38ZPtx8rOmcNsVBVqgIRIDdAR+Eb98pZaqxm4237Ey7MMbwJ0zlX98e6nTZYhIGAj/xAqh9t5BHnj6CKuLsri2dKbT5YiIjElMB/gPtx2ltWeAr95UqpWJIhJxYjbAT7T08OjfarhlFLO+RUTC0YR64MaYGqAT8AFDF2qyh6tvnJn1vWn0s75FRMJJME5ivtVa2xSE15k0u4638KfXvNyzseQNC2NERCJJzLVQ/H7LP//xADPTk7nr6iKnyxERGbeJBrgF/mKM2WWMuSsYBYXaf792ir0n2rhv04Kg3KhBRMQpE02wddbaU8aYGcBWY8xBa+0L5z4gEOx3ARQUFExwdxPTN+jjW08dYnFuOresCO6NGkREJtuEjsCttacCHxuA3wFXXOAxm621HmutZ/r06RPZ3YQ9/OIxTrb18pWbFmkutohEvHEHuDFmqjFm2pnPgU1AZbAKC7bGzn5+uO0o15bOZG1xjtPliIhM2ERaKDOB3wUWwCQAP7fWPhWUqkLg/jOzvm9Y6HQpIiJBMe4At9ZWA0uDWEvIHKrv5D931nLnWjdFQbznpIiIk2LiMsKvlx1gWkoid28ocboUEZGgifoAf+5QAy8cbuSzG0rCbta3iMhERHWAD/n8fP1PB3Bnp3LH6kKnyxERCaqoDvBf7jzBkYYuvnTjooiY9S0iMhZRm2odfYM8sPUwV87NYpNmfYtIFIrateQ/3FZFc/cAj2nWt4hEqag8Aj/R0sMjLx7j3SvmcFmeZn2LSHSKygD/5lMHiYuDL1ynWd8iEr2iLsB3HW/lj695uevqYmZnTHG6HBGRkImqALfW8q9/2s+Macl8XLO+RSTKRVWA//E1L6/WtnHfdQuYmhy152dFRIAoCvC+QR/f2HKQ0tma9S0isSFqAvzRv9Vwsq2Xr960iHjN+haRGBAVAd7U1c8Pth1l46KZrJ2nWd8iEhuiIsAf2HqYvkEfX7pRs75FJHZEfIAfPt3JL16p5f2rCynWrG8RiSERH+Bf/9MB0pITNOtbRGJORAf484cbeT4w69s1VbO+RSS2RGyAD8/63k9hdip3rNGsbxGJPREb4L8qr+Pw6S6+dMNCkhPinS5HRGTSRWSAd/YNcv/WQ1zhzuK6xbOcLkdExBERud78weeqaOoa4JEPLtKsbxGJWRF3BF7X2sOPXzzGu5fP4fK8TKfLERFxTMQF+LeeOkScgfs061tEYlxEBfju2lb+sPcUd11VRG6mZn2LSGyLmAC31vKvf9zP9GnJfPzvip0uR0TEcRET4H+q8LK7to0vbNKsbxERiJAA7xv08c2nDrJodjq3rNSsbxERiJDLCB9/qYYTLb088dHLNetbRCQgIo7Ac9KSudWTxzrN+hYROSsijsBvWZmn1omIyHki4ghcRETeTAEuIhKhFOAiIhFqQgFujLneGHPIGHPUGPPFYBUlIiIjG3eAG2PigR8ANwClwG3GmNJgFSYiIpc2kSPwK4Cj1tpqa+0A8Evg5uCUJSIiI5lIgM8BTpzz97rANhERmQQTCfALLYm0b3qQMXcZY8qNMeWNjY0T2J2IiJxrIgt56oD8c/6eB5w6/0HW2s3AZgBjTKMx5vgE9hkOcoAmp4sII3o/Xqf34o30frzRRN6PC9653Vj7poPmUTHGJACHgQ3ASWAncLu1dt84C4wIxphya63H6TrChd6P1+m9eCO9H28Uivdj3Efg1tohY8yngT8D8cAj0R7eIiLhZEKzUKy1ZUBZkGoREZEx0ErMsdvsdAFhRu/H6/RevJHejzcK+vsx7h64iIg4S0fgIiIRSgE+SsaYfGPMNmPMAWPMPmPM3U7X5DRjTLwx5lVjzB+drsVpxphMY8xvjDEHA98ja5yuySnGmHsDPyOVxphfGGNSnK5pMhljHjHGNBhjKs/ZlmWM2WqMORL46ArGvhTgozcEfN5auwhYDXxKs1+4GzjgdBFh4rvAU9bahcBSYvR9McbMAT4LeKy1Sxi+Qu29zlY16R4Drj9v2xeBZ6y1JcAzgb9PmAJ8lKy1Xmvt7sDnnQz/gMbs6ABjTB5wE/Bjp2txmjEmHbgaeBjAWjtgrW1ztChnJQBTAmtFUrnAAr9oZq19AWg5b/PNwOOBzx8H3hmMfSnAx8EY4waWAzscLsVJ3wH+F+B3uI5wUAQ0Ao8GWko/NsZMdbooJ1hrTwL/DtQCXqDdWvsXZ6sKCzOttV4YPhgEZgTjRRXgY2SMSQN+C9xjre1wuh4nGGPeBjRYa3c5XUuYSABWAA9aa5cD3QTpV+RIE+jt3gzMBXKBqcaY9ztbVfRSgI+BMSaR4fB+wlr7pNP1OGgd8A5jTA3DY4SvMcb8zNmSHFUH1Flrz/xG9huGAz0WbQSOWWsbrbWDwJPAWodrCgenjTGzAQIfG4LxogrwUTLGGIZ7nAestfc7XY+TrLVfstbmWWvdDJ+getZaG7NHWdbaeuCEMWZBYNMGYL+DJTmpFlhtjEkN/MxsIEZP6J7nD8Cdgc/vBH4fjBed0FL6GLMOuAOoMMbsCWz7cmCcgMhngCeMMUlANfAhh+txhLV2hzHmN8Buhq/cepUYW5FpjPkFsB7IMcbUAV8DvgH8yhjzEYb/kXtPUPallZgiIpFJLRQRkQilABcRiVAKcBGRCKUAFxGJUApwEZEIpQAXEYlQCnARkQilABcRiVD/H7nl5vZHPo8tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,11)\n",
    "y = x*np.random.randint(1,5,10)\n",
    "\n",
    "fig,ax=plt.subplots()\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T06:03:03.165100Z",
     "start_time": "2020-12-14T06:03:03.011510Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2bdcaacdeb0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 这种方式不太适应\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure的组成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Figure：顶层级，用来容纳所有绘图元素\n",
    "\n",
    "- Axes：matplotlib宇宙的核心，容纳了大量元素用来构造一幅幅子图，一个figure可以由一个或多个子图组成\n",
    "\n",
    "- Axis：axes的下属层级，用于处理所有和坐标轴，网格有关的元素\n",
    "\n",
    "- Tick：axis的下属层级，用来处理所有和刻度有关的元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两种绘图接口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib提供了两种最常用的绘图接口\n",
    "\n",
    "- 显式创建figure和axes，在上面调用绘图方法，也被称为OO模式（object-oriented style)\n",
    "\n",
    "- 依赖pyplot自动创建figure和axes，并绘图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T06:03:03.710116Z",
     "start_time": "2020-12-14T06:03:03.476197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2bdcab1c460>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "ax.plot(x, x, label=\"linear\")\n",
    "ax.plot(x, x**2, label=\"square\")\n",
    "ax.plot(x, x**3, label=\"cubic\")\n",
    "\n",
    "ax.set_title(\"Simple Plot\")\n",
    "ax.set_xlabel(\"x label\")\n",
    "ax.set_ylabel(\"y label\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T06:03:03.994530Z",
     "start_time": "2020-12-14T06:03:03.762211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2bdcabae9d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "plt.plot(x, x, label=\"linear\")\n",
    "plt.plot(x, x**2, label=\"square\")\n",
    "plt.plot(x, x**3, label=\"cubic\")\n",
    "\n",
    "plt.title(\"Simple Plot\")\n",
    "plt.xlabel(\"x label\")\n",
    "plt.ylabel(\"y label\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "https://matplotlib.org/tutorials/introductory/usage.html#sphx-glr-tutorials-introductory-usage-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "你在工作或学习中通常何时会用到数据可视化，希望通过可视化达到什么目的？\n",
    "\n",
    "1、做数据探索的过程中，数据报告展示，还有做一些脚本工具开发的时候会用到。\n",
    "2、希望能够熟练掌握各种API的用法，熟练使用API文档，能够做一些酷炫的复杂的图表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "nbconvert_exporter": "python",
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  "toc": {
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